雾霾天气车辆检测的改进YOLOv3算法

针对传统YOLOv3算法中存在检测框定位不精确的问题，提出了一种改进的YOLOv3算法用来重新估计检测框位置，提高智能汽车在雾霾交通环境下的定位精度。首先运用图像去雾算法对采集到的图片进行预处理，然后构造定位置信度替代分类置信度作为参考项来选择估计检测框位置，并改进非极大值抑制（NMS）算法，引入软化非极大值抑制（soft-NMS），最后使用加权平均的方式来更新坐标位置，以达到提高定位精度的目的。实验结果表明，先经过单尺度retinex去雾算法处理图片，再通过改进的YOLOv3算法进行车辆检测，与使用原始的YOLOv3算法进行检测相比平均精度均值mAP（mean average precision）提高了0.44%，在满足检测实时性的同时，能够检测到更多的目标，对检测车辆的定位也更加精确。

Improved YOLOv3 algorithm for vehicle detection in hazy weather

Traditional YOLOv3 algorithm has the problem of inaccurate detection frame positioning. In this paper, an improved YOLOv3 algorithm was proposed to re-estimate the detection frame position and improve the positioning accuracy of smart cars in hazy traffic environment. Firstly, the collected images were preprocessed using image dehazing algorithms. Then, to improve positioning accuracy, estimated test box position was selected by constructing positioning confidence as the reference to replace classification confidence, and the non-maximum suppression (NMS) algorithm was improved. Finally, the soft-NMS was introduced, and the coordinates were updated using the weighted average. The experimental results show that compared with the original YOLOv3 algorithm, the mAP(mean average precision) of improved YOLOv3 algorithm increased by 0.44%, which could detect more targets and locate the detected vehicles more accurately in real-time detection.